from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 53.850834 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 30.570622 |
| KNeighborsClassifier_kd_tree | 0.0 | 7.0 | 25.276314 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 51.245386 |
| KMeans_tall | 0.0 | 1.0 | 42.475878 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 19.272617 |
| KMeans_short | 0.0 | 0.0 | 23.884309 |
| daal4py_KMeans_short | 0.0 | 0.0 | 11.016962 |
| LogisticRegression | 0.0 | 1.0 | 4.300081 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 57.582113 |
| Ridge | 0.0 | 0.0 | 49.973108 |
| daal4py_Ridge | 0.0 | 0.0 | 16.137633 |
| total | 0.0 | 33.0 | 25.658883 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.148 | 0.004 | 1000000 | 1000000 | 100 | brute | -1 | 1 | 0.983 | 0.982 | 0.494 | 0.005 | 0.299 | 0.008 | See |
| 1 | KNeighborsClassifier | predict | 26.411 | 0.853 | 1000000 | 1000 | 100 | brute | -1 | 1 | 0.983 | 0.982 | 1.993 | 0.033 | 13.252 | 0.481 | See |
| 2 | KNeighborsClassifier | predict | 0.171 | 0.018 | 1000000 | 1 | 100 | brute | -1 | 1 | 0.983 | 0.982 | 0.085 | 0.001 | 2.008 | 0.212 | See |
| 3 | KNeighborsClassifier | fit | 0.147 | 0.003 | 1000000 | 1000000 | 100 | brute | -1 | 5 | 0.983 | 0.982 | 0.483 | 0.002 | 0.305 | 0.006 | See |
| 4 | KNeighborsClassifier | predict | 34.575 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 5 | 0.983 | 0.982 | 2.110 | 0.095 | 16.388 | 0.736 | See |
| 5 | KNeighborsClassifier | predict | 0.176 | 0.016 | 1000000 | 1 | 100 | brute | -1 | 5 | 0.983 | 0.982 | 0.087 | 0.001 | 2.023 | 0.185 | See |
| 6 | KNeighborsClassifier | fit | 0.144 | 0.002 | 1000000 | 1000000 | 100 | brute | -1 | 100 | 0.983 | 0.982 | 0.495 | 0.001 | 0.291 | 0.005 | See |
| 7 | KNeighborsClassifier | predict | 36.478 | 0.000 | 1000000 | 1000 | 100 | brute | -1 | 100 | 0.983 | 0.982 | 2.139 | 0.102 | 17.050 | 0.816 | See |
| 8 | KNeighborsClassifier | predict | 0.194 | 0.017 | 1000000 | 1 | 100 | brute | -1 | 100 | 0.983 | 0.982 | 0.089 | 0.003 | 2.178 | 0.210 | See |
| 9 | KNeighborsClassifier | fit | 0.148 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 1 | 0.983 | 0.982 | 0.482 | 0.002 | 0.307 | 0.007 | See |
| 10 | KNeighborsClassifier | predict | 13.502 | 0.228 | 1000000 | 1000 | 100 | brute | 1 | 1 | 0.983 | 0.982 | 2.079 | 0.099 | 6.495 | 0.328 | See |
| 11 | KNeighborsClassifier | predict | 0.192 | 0.003 | 1000000 | 1 | 100 | brute | 1 | 1 | 0.983 | 0.982 | 0.090 | 0.003 | 2.131 | 0.070 | See |
| 12 | KNeighborsClassifier | fit | 0.147 | 0.003 | 1000000 | 1000000 | 100 | brute | 1 | 5 | 0.983 | 0.982 | 0.489 | 0.001 | 0.301 | 0.006 | See |
| 13 | KNeighborsClassifier | predict | 23.715 | 0.127 | 1000000 | 1000 | 100 | brute | 1 | 5 | 0.983 | 0.982 | 1.978 | 0.030 | 11.987 | 0.195 | See |
| 14 | KNeighborsClassifier | predict | 0.197 | 0.002 | 1000000 | 1 | 100 | brute | 1 | 5 | 0.983 | 0.982 | 0.084 | 0.000 | 2.331 | 0.024 | See |
| 15 | KNeighborsClassifier | fit | 0.140 | 0.002 | 1000000 | 1000000 | 100 | brute | 1 | 100 | 0.983 | 0.982 | 0.483 | 0.003 | 0.289 | 0.005 | See |
| 16 | KNeighborsClassifier | predict | 23.536 | 0.433 | 1000000 | 1000 | 100 | brute | 1 | 100 | 0.983 | 0.982 | 2.168 | 0.079 | 10.858 | 0.443 | See |
| 17 | KNeighborsClassifier | predict | 0.195 | 0.002 | 1000000 | 1 | 100 | brute | 1 | 100 | 0.983 | 0.982 | 0.091 | 0.003 | 2.141 | 0.075 | See |
| 18 | KNeighborsClassifier | fit | 0.060 | 0.001 | 1000000 | 1000000 | 2 | brute | -1 | 1 | 0.983 | 0.982 | 0.113 | 0.001 | 0.534 | 0.014 | See |
| 19 | KNeighborsClassifier | predict | 23.413 | 0.137 | 1000000 | 1000 | 2 | brute | -1 | 1 | 0.983 | 0.982 | 0.329 | 0.009 | 71.100 | 1.996 | See |
| 20 | KNeighborsClassifier | predict | 0.022 | 0.003 | 1000000 | 1 | 2 | brute | -1 | 1 | 0.983 | 0.982 | 0.007 | 0.001 | 3.294 | 0.654 | See |
| 21 | KNeighborsClassifier | fit | 0.064 | 0.003 | 1000000 | 1000000 | 2 | brute | -1 | 5 | 0.983 | 0.982 | 0.111 | 0.000 | 0.576 | 0.024 | See |
| 22 | KNeighborsClassifier | predict | 32.639 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 5 | 0.983 | 0.982 | 0.334 | 0.010 | 97.620 | 2.807 | See |
| 23 | KNeighborsClassifier | predict | 0.026 | 0.001 | 1000000 | 1 | 2 | brute | -1 | 5 | 0.983 | 0.982 | 0.006 | 0.000 | 4.080 | 0.270 | See |
| 24 | KNeighborsClassifier | fit | 0.062 | 0.000 | 1000000 | 1000000 | 2 | brute | -1 | 100 | 0.983 | 0.982 | 0.109 | 0.005 | 0.575 | 0.025 | See |
| 25 | KNeighborsClassifier | predict | 33.610 | 0.000 | 1000000 | 1000 | 2 | brute | -1 | 100 | 0.983 | 0.982 | 0.371 | 0.016 | 90.671 | 3.845 | See |
| 26 | KNeighborsClassifier | predict | 0.026 | 0.002 | 1000000 | 1 | 2 | brute | -1 | 100 | 0.983 | 0.982 | 0.007 | 0.001 | 3.749 | 0.599 | See |
| 27 | KNeighborsClassifier | fit | 0.062 | 0.001 | 1000000 | 1000000 | 2 | brute | 1 | 1 | 0.983 | 0.982 | 0.108 | 0.003 | 0.571 | 0.018 | See |
| 28 | KNeighborsClassifier | predict | 10.591 | 0.057 | 1000000 | 1000 | 2 | brute | 1 | 1 | 0.983 | 0.982 | 0.315 | 0.015 | 33.625 | 1.582 | See |
| 29 | KNeighborsClassifier | predict | 0.016 | 0.000 | 1000000 | 1 | 2 | brute | 1 | 1 | 0.983 | 0.982 | 0.006 | 0.001 | 2.781 | 0.320 | See |
| 30 | KNeighborsClassifier | fit | 0.064 | 0.000 | 1000000 | 1000000 | 2 | brute | 1 | 5 | 0.983 | 0.982 | 0.105 | 0.003 | 0.606 | 0.019 | See |
| 31 | KNeighborsClassifier | predict | 19.702 | 0.261 | 1000000 | 1000 | 2 | brute | 1 | 5 | 0.983 | 0.982 | 0.296 | 0.005 | 66.537 | 1.345 | See |
| 32 | KNeighborsClassifier | predict | 0.022 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 5 | 0.983 | 0.982 | 0.006 | 0.001 | 3.445 | 0.758 | See |
| 33 | KNeighborsClassifier | fit | 0.075 | 0.003 | 1000000 | 1000000 | 2 | brute | 1 | 100 | 0.983 | 0.982 | 0.103 | 0.002 | 0.723 | 0.028 | See |
| 34 | KNeighborsClassifier | predict | 19.745 | 0.256 | 1000000 | 1000 | 2 | brute | 1 | 100 | 0.983 | 0.982 | 0.352 | 0.006 | 56.131 | 1.207 | See |
| 35 | KNeighborsClassifier | predict | 0.021 | 0.001 | 1000000 | 1 | 2 | brute | 1 | 100 | 0.983 | 0.982 | 0.006 | 0.000 | 3.417 | 0.278 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 3.242 | 0.235 | 1000000 | 1000000 | 10 | kd_tree | -1 | 1 | 0.979 | 0.986 | 0.792 | 0.019 | 4.092 | 0.312 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.483 | 0.012 | 1000000 | 1000 | 10 | kd_tree | -1 | 1 | 0.979 | 0.986 | 0.118 | 0.011 | 4.103 | 0.388 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 10 | kd_tree | -1 | 1 | 0.979 | 0.986 | 0.000 | 0.000 | 9.761 | 4.050 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 3.117 | 0.126 | 1000000 | 1000000 | 10 | kd_tree | -1 | 5 | 0.979 | 0.986 | 0.794 | 0.024 | 3.927 | 0.197 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.933 | 0.019 | 1000000 | 1000 | 10 | kd_tree | -1 | 5 | 0.979 | 0.986 | 0.204 | 0.005 | 4.565 | 0.149 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.003 | 1000000 | 1 | 10 | kd_tree | -1 | 5 | 0.979 | 0.986 | 0.000 | 0.000 | 14.856 | 9.838 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 3.140 | 0.207 | 1000000 | 1000000 | 10 | kd_tree | -1 | 100 | 0.979 | 0.986 | 0.759 | 0.009 | 4.137 | 0.277 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.670 | 0.078 | 1000000 | 1000 | 10 | kd_tree | -1 | 100 | 0.979 | 0.986 | 0.577 | 0.011 | 4.625 | 0.163 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.007 | 0.001 | 1000000 | 1 | 10 | kd_tree | -1 | 100 | 0.979 | 0.986 | 0.001 | 0.000 | 7.961 | 2.615 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.004 | 0.094 | 1000000 | 1000000 | 10 | kd_tree | 1 | 1 | 0.979 | 0.986 | 0.839 | 0.037 | 3.579 | 0.192 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.765 | 0.026 | 1000000 | 1000 | 10 | kd_tree | 1 | 1 | 0.979 | 0.986 | 0.105 | 0.002 | 7.284 | 0.275 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 1 | 0.979 | 0.986 | 0.000 | 0.000 | 4.881 | 2.320 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.194 | 0.211 | 1000000 | 1000000 | 10 | kd_tree | 1 | 5 | 0.979 | 0.986 | 0.833 | 0.040 | 3.835 | 0.312 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.365 | 0.016 | 1000000 | 1000 | 10 | kd_tree | 1 | 5 | 0.979 | 0.986 | 0.199 | 0.001 | 6.847 | 0.085 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | kd_tree | 1 | 5 | 0.979 | 0.986 | 0.000 | 0.000 | 4.340 | 2.163 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.384 | 0.053 | 1000000 | 1000000 | 10 | kd_tree | 1 | 100 | 0.979 | 0.986 | 0.828 | 0.011 | 4.086 | 0.085 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 4.864 | 0.136 | 1000000 | 1000 | 10 | kd_tree | 1 | 100 | 0.979 | 0.986 | 0.608 | 0.032 | 7.994 | 0.480 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.001 | 1000000 | 1 | 10 | kd_tree | 1 | 100 | 0.979 | 0.986 | 0.001 | 0.000 | 5.270 | 1.950 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1.743 | 0.025 | 1000000 | 1000000 | 2 | kd_tree | -1 | 1 | 0.979 | 0.986 | 0.504 | 0.013 | 3.458 | 0.102 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.031 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 1 | 0.979 | 0.986 | 0.001 | 0.000 | 30.166 | 13.628 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 2 | kd_tree | -1 | 1 | 0.979 | 0.986 | 0.000 | 0.000 | 20.994 | 16.563 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1.655 | 0.043 | 1000000 | 1000000 | 2 | kd_tree | -1 | 5 | 0.979 | 0.986 | 0.509 | 0.009 | 3.252 | 0.104 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.033 | 0.001 | 1000000 | 1000 | 2 | kd_tree | -1 | 5 | 0.979 | 0.986 | 0.001 | 0.001 | 24.857 | 12.206 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | kd_tree | -1 | 5 | 0.979 | 0.986 | 0.000 | 0.000 | 22.256 | 16.304 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1.710 | 0.040 | 1000000 | 1000000 | 2 | kd_tree | -1 | 100 | 0.979 | 0.986 | 0.494 | 0.008 | 3.461 | 0.097 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.055 | 0.003 | 1000000 | 1000 | 2 | kd_tree | -1 | 100 | 0.979 | 0.986 | 0.007 | 0.001 | 7.981 | 1.048 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | kd_tree | -1 | 100 | 0.979 | 0.986 | 0.000 | 0.000 | 21.255 | 15.997 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1.668 | 0.046 | 1000000 | 1000000 | 2 | kd_tree | 1 | 1 | 0.979 | 0.986 | 0.509 | 0.014 | 3.279 | 0.126 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.030 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 1 | 0.979 | 0.986 | 0.001 | 0.000 | 27.042 | 11.379 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 1 | 0.979 | 0.986 | 0.000 | 0.000 | 6.202 | 5.290 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1.640 | 0.030 | 1000000 | 1000000 | 2 | kd_tree | 1 | 5 | 0.979 | 0.986 | 0.480 | 0.021 | 3.419 | 0.165 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.028 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 5 | 0.979 | 0.986 | 0.001 | 0.000 | 21.642 | 8.107 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 5 | 0.979 | 0.986 | 0.000 | 0.000 | 4.799 | 3.049 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1.636 | 0.074 | 1000000 | 1000000 | 2 | kd_tree | 1 | 100 | 0.979 | 0.986 | 0.508 | 0.029 | 3.220 | 0.235 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.060 | 0.001 | 1000000 | 1000 | 2 | kd_tree | 1 | 100 | 0.979 | 0.986 | 0.008 | 0.001 | 7.693 | 0.546 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | kd_tree | 1 | 100 | 0.979 | 0.986 | 0.000 | 0.000 | 5.733 | 4.792 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.683 | 0.015 | 1000000 | 1000000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.480 | 0.023 | 1.422 | 0.075 | See |
| 1 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.249 | 1.916 | See |
| 2 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.164 | 1.932 | See |
| 3 | KMeans_tall | fit | 0.574 | 0.031 | 1000000 | 1000000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.403 | 0.018 | 1.425 | 0.101 | See |
| 4 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1000 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 1.221 | 0.936 | See |
| 5 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 2 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.099 | 1.886 | See |
| 6 | KMeans_tall | fit | 6.520 | 0.177 | 1000000 | 1000000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 3.230 | 0.050 | 2.018 | 0.063 | See |
| 7 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.167 | 1.865 | See |
| 8 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | k-means++ | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 1.296 | 1.542 | See |
| 9 | KMeans_tall | fit | 6.192 | 0.187 | 1000000 | 1000000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 2.869 | 0.027 | 2.158 | 0.068 | See |
| 10 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 2.140 | 1.179 | See |
| 11 | KMeans_tall | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | full | random | 30 | 3 | 1 | 0.0 | 30 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 1.783 | 1.302 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | algorithm | init | max_iter | n_clusters | n_init | tol | n_iter_sklearn | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.369 | 0.047 | 10000 | 10000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 26 | 0.005 | 27 | 0.007 | 0.114 | 0.003 | 3.230 | 0.427 | See |
| 1 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 26 | 0.005 | 27 | 0.007 | 0.001 | 0.000 | 1.157 | 0.341 | See |
| 2 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | k-means++ | 30 | 300 | 1 | 0.0 | 26 | 0.005 | 27 | 0.007 | 0.000 | 0.000 | 2.122 | 1.641 | See |
| 3 | KMeans_short | fit | 0.137 | 0.003 | 10000 | 10000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.005 | 30 | 0.007 | 0.050 | 0.004 | 2.761 | 0.212 | See |
| 4 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1000 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.005 | 30 | 0.007 | 0.001 | 0.000 | 1.072 | 0.265 | See |
| 5 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 2 | full | random | 30 | 300 | 1 | 0.0 | 30 | 0.005 | 30 | 0.007 | 0.000 | 0.000 | 2.012 | 1.636 | See |
| 6 | KMeans_short | fit | 0.921 | 0.054 | 10000 | 10000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 15 | 0.005 | 24 | 0.007 | 0.459 | 0.041 | 2.006 | 0.216 | See |
| 7 | KMeans_short | predict | 0.003 | 0.001 | 10000 | 1000 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 15 | 0.005 | 24 | 0.007 | 0.002 | 0.000 | 1.872 | 0.584 | See |
| 8 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | k-means++ | 30 | 300 | 1 | 0.0 | 15 | 0.005 | 24 | 0.007 | 0.000 | 0.000 | 2.298 | 1.489 | See |
| 9 | KMeans_short | fit | 0.269 | 0.035 | 10000 | 10000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 19 | 0.005 | 30 | 0.007 | 0.208 | 0.058 | 1.295 | 0.399 | See |
| 10 | KMeans_short | predict | 0.002 | 0.001 | 10000 | 1000 | 100 | full | random | 30 | 300 | 1 | 0.0 | 19 | 0.005 | 30 | 0.007 | 0.001 | 0.000 | 1.635 | 0.807 | See |
| 11 | KMeans_short | predict | 0.000 | 0.000 | 10000 | 1 | 100 | full | random | 30 | 300 | 1 | 0.0 | 19 | 0.005 | 30 | 0.007 | 0.000 | 0.000 | 2.043 | 1.324 | See |
Shared hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 11.521 | 0.045 | 1000000 | 1000000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.29 | 11.700 | 0.087 | 0.985 | 0.008 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.29 | 0.000 | 0.000 | 0.827 | 0.436 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [20] | 0.29 | 0.000 | 0.000 | 0.346 | 0.345 | See |
| 3 | LogisticRegression | fit | 0.796 | 0.028 | 1000 | 1000 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [26] | 0.29 | 0.771 | 0.033 | 1.033 | 0.056 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [26] | 0.29 | 0.003 | 0.001 | 0.629 | 0.144 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0 | 0 | False | [26] | 0.29 | 0.001 | 0.000 | 0.119 | 0.089 | See |
Shared hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | n_iter | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1.824 | 0.034 | 100000 | 100000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.985 | 0.027 | 1.853 | 0.062 | See |
| 1 | Ridge | predict | 0.001 | 0.000 | 100000 | 1000 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.001 | 0.000 | 0.832 | 0.443 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 100000 | 1 | 1000 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.598 | 0.652 | See |
| 3 | Ridge | fit | 1.178 | 0.027 | 1000000 | 1000000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.249 | 0.003 | 4.728 | 0.121 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.564 | 0.400 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | 1.0 | True | True | NaN | False | NaN | auto | 0.001 | NaN | 1.0 | 0.000 | 0.000 | 0.597 | 0.698 | See |
Shared hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | False |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
{
"system_info": {
"python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1",
"setuptools": "49.6.0.post20210108",
"sklearn": "0.24.1",
"numpy": "1.20.2",
"scipy": "1.6.2",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": null,
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
"prefix": "libopenblas",
"user_api": "blas",
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"version": "0.3.12",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}